{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading the ECCOv4 state estimate fields on the native model grid\n",
    "\n",
    "## Objectives\n",
    "\n",
    "Introduce several methods for loading the ECCOv4 state estimate files on the native model grid.\n",
    "\n",
    "## Introduction \n",
    "\n",
    "ECCOv4 native-grid state estimate fields are packaged together as NetCDF files.  We have been improving how these files are created, what fields they contain, and how they are distributed in directories.  As of this writing, Sep 2019, the latest version of these files can be found here:\n",
    "\n",
    "https://ecco.jpl.nasa.gov/drive/files/Version4/Release3_alt (po.daac drive, recommended)\n",
    "\n",
    "https://web.corral.tacc.utexas.edu/OceanProjects/ECCO/ECCOv4/Release3_alt/  (mirror at U. Texas, Austin)\n",
    "\n",
    "This tutorial document is current with the files in the above directories as of September 2019.\n",
    "\n",
    "\n",
    "## NetCDF File Format\n",
    "\n",
    "ECCOv4 state estimate fields are provided as NetCDF files with one variable and one year per file.  State estimate fields are provided as **monthly means**, **daily means** and **monthly snapshots**.  The file directories for NetCDF files should look something like (although exact naming may vary):\n",
    "\n",
    "* /nctiles_monthly/**VARIABLE_NAME**/**VARIABLE_NAME_YYYY**.nc\n",
    "* /nctiles_monthly_snapshots/**VARIABLE_NAME**/**VARIABLE_NAME_YYYY**.nc\n",
    "* /nctiles_daily/**VARIABLE_NAME**/**VARIABLE_NAME_YYYY**.nc\n",
    "\n",
    "While the model grid files are provided here:\n",
    "\n",
    "* /nctiles_grid/ECCOv4r3_grid.nc\n",
    "\n",
    "Typical file sizes are:\n",
    "~~~\n",
    "3D monthly-mean and monthly-snapshot fields:  265mb (50 levels x 12 months x 25 years x 13 tiles)\n",
    "2D monthly-mean fields                     :    6mb ( 1 level  x 12 months x 25 years x 13 tiles)\n",
    "2D daily-mean fields                       :  150mb ( 1 level  x 365 days  x 12 years x 13 tiles)\n",
    "~~~\n",
    "\n",
    "The advantages of aggregating one year of data into a single NetCDF file is that the I/O time per data element.  One nice feature of using the ``xarray`` and ``Dask`` libraries is that we do not have to load the entire file contents into RAM to work with them.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Two methods to load one ECCOv4 NetCDF file \n",
    "\n",
    "In ECCO NetCDF files, all 13 tiles for a given year are aggregated into a single file.  Therefore, we can use the ``open_dataset`` routine from ``xarray`` to open a single NetCDF variable file.\n",
    "\n",
    "Alternatively, the subroutine ``load_ecco_var_from_years_nc`` allows you to optionally specify a subset of vertical levels or tiles to load using optional arguments.\n",
    "\n",
    "We'll show both methods.  First ``open_dataset`` then ``load_ecco_var_from_years_nc``\n",
    "\n",
    "### First set up the environment,  load model grid parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import xarray as xr\n",
    "import sys\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Import the ecco_v4_py library into Python\n",
    "## =========================================\n",
    "\n",
    "## -- If ecco_v4_py is not installed in your local Python library, \n",
    "##    tell Python where to find it.  For example, if your ecco_v4_py\n",
    "##    files are in /Users/ifenty/ECCOv4-py/ecco_v4_py, then use:\n",
    "sys.path.append('/home/ifenty/ECCOv4-py')\n",
    "\n",
    "import ecco_v4_py as ecco"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Set top-level file directory for the ECCO NetCDF files\n",
    "## =================================================================\n",
    "# base_dir = '/home/username/'\n",
    "base_dir = '/home/ifenty/ECCOv4-release/'\n",
    "\n",
    "## define a high-level directory for ECCO fields\n",
    "ECCO_dir = base_dir + '/Release3_alt/'\n",
    "\n",
    "## define the directory with the model grid\n",
    "grid_dir = ECCO_dir + 'nctiles_grid/'\n",
    "\n",
    "## load the grid\n",
    "grid = ecco.load_ecco_grid_nc(grid_dir, 'ECCOv4r3_grid.nc')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading a single ECCOv4 variable NetCDF file using ``open_dataset``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.DataArray 'SSH' (time: 12, tile: 13, j: 90, i: 90)>\n",
       "[1263600 values with dtype=float32]\n",
       "Coordinates:\n",
       "  * j        (j) int32 0 1 2 3 4 5 6 7 8 9 10 ... 80 81 82 83 84 85 86 87 88 89\n",
       "  * i        (i) int32 0 1 2 3 4 5 6 7 8 9 10 ... 80 81 82 83 84 85 86 87 88 89\n",
       "    XC       (tile, j, i) float32 ...\n",
       "    YC       (tile, j, i) float32 ...\n",
       "    rA       (tile, j, i) float32 ...\n",
       "  * tile     (tile) int32 0 1 2 3 4 5 6 7 8 9 10 11 12\n",
       "    iter     (time) int32 ...\n",
       "  * time     (time) datetime64[ns] 2010-01-16T12:00:00 ... 2010-12-16T12:00:00\n",
       "Attributes:\n",
       "    units:          m\n",
       "    long_name:      Surface Height Anomaly adjusted with global steric height...\n",
       "    standard_name:  sea_surface_height"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SSH_dir = ECCO_dir + '/nctiles_monthly/SSH/'\n",
    "SSH_dataset = xr.open_dataset(SSH_dir + '/SSH_2010.nc')\n",
    "SSH_dataset.SSH"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "``SSH_dataset.SSH`` contains 12 months of data across 13 tiles.\n",
    "\n",
    "Let's plot the first time record of this file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "SSH  = SSH_dataset.SSH.isel(time=0)\n",
    "# mask to nan where hFacC(k=0) = 0\n",
    "SSH  = SSH.where(grid.hFacC.isel(k=0))\n",
    "\n",
    "fig  = plt.figure(figsize=(16,7))\n",
    "ecco.plot_proj_to_latlon_grid(grid.XC, grid.YC, SSH, show_colorbar=True, cmin=-1.5, cmax=1.5);plt.title('SSH [m]');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading a single ECCOv4 variable NetCDF file using ``load_ecco_var_from_years_nc``\n",
    "\n",
    "We'll now load the same 2010 SSH file using ``load_ecco_var_from_years_nc``.  This time we specify the directory containing the NetCDF file, the variable that want to load and the year of interest. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:    (i: 90, j: 90, nv: 2, tile: 13, time: 12)\n",
       "Coordinates:\n",
       "  * j          (j) int32 0 1 2 3 4 5 6 7 8 9 ... 80 81 82 83 84 85 86 87 88 89\n",
       "  * i          (i) int32 0 1 2 3 4 5 6 7 8 9 ... 80 81 82 83 84 85 86 87 88 89\n",
       "    XC         (tile, j, i) float32 -111.60647 -111.303 ... -111.86579\n",
       "    YC         (tile, j, i) float32 -88.24259 -88.382515 ... -88.07871 -88.10267\n",
       "    rA         (tile, j, i) float32 362256450.0 363300960.0 ... 361119100.0\n",
       "  * tile       (tile) int32 0 1 2 3 4 5 6 7 8 9 10 11 12\n",
       "    time_bnds  (time, nv) datetime64[ns] 2010-01-01 2010-02-01 ... 2011-01-01\n",
       "    iter       (time) int32 158532 159204 159948 160668 ... 165084 165804 166548\n",
       "  * time       (time) datetime64[ns] 2010-01-16T12:00:00 ... 2010-12-16T12:00:00\n",
       "Dimensions without coordinates: nv\n",
       "Data variables:\n",
       "    SSH        (time, tile, j, i) float32 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# single year:  2010\n",
    "SSH_dataset_2010 = ecco.load_ecco_var_from_years_nc(SSH_dir, \\\n",
    "                           'SSH', years_to_load = [2010]).load()\n",
    "SSH_dataset_2010.attrs = []\n",
    "SSH_dataset_2010"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading a subset of single ECCOv4 variable NetCDF file using ``load_ecco_var_from_years_nc``\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One benefit of using  ``load_ecco_var_from_years_nc`` over ``open_dataset`` is that you can optionally specify a subset of vertical levels or tiles to load using optional arguments with the *tiles_to_load* and *k_subset* optional arguments.  \n",
    "\n",
    "By default \n",
    "\n",
    "* *tiles_to_load* = [0, 1, ... 12]\n",
    "* k_subset = []     \n",
    "\n",
    "To load a subset of tiles, specify the desired tile indices in *tiles_to_load*.  For example, to load tiles 3,4 and 5:\n",
    "~~~~\n",
    "tiles_to_load = [3, 4, 5]\n",
    "~~~~\n",
    "\n",
    "To load a subset of depth levels, specify the desired depth level indices in *k_subset*.  For example, to load the top 5 levels:\n",
    "~~~~\n",
    "k_subset = [0,1,2,3,4]\n",
    "~~~~\n",
    "\n",
    "In the following example we load ``THETA`` for tiles 7,8,9 and depth levels 0:34."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:    (i: 90, j: 90, k: 5, nv: 2, tile: 3, time: 12)\n",
       "Coordinates:\n",
       "  * j          (j) int32 0 1 2 3 4 5 6 7 8 9 ... 80 81 82 83 84 85 86 87 88 89\n",
       "  * i          (i) int32 0 1 2 3 4 5 6 7 8 9 ... 80 81 82 83 84 85 86 87 88 89\n",
       "  * k          (k) int32 0 1 2 3 4\n",
       "    Z          (k) float32 -5.0 -15.0 -25.0 -35.0 -45.0\n",
       "    PHrefC     (k) float32 49.05 147.15 245.25 343.35 441.45\n",
       "    drF        (k) float32 10.0 10.0 10.0 10.0 10.0\n",
       "    XC         (tile, j, i) float32 142.16208 142.22801 ... -115.5476 -115.18083\n",
       "    YC         (tile, j, i) float32 67.47211 67.33552 ... -80.43542 -80.43992\n",
       "    rA         (tile, j, i) float32 212633870.0 351016450.0 ... 47093870.0\n",
       "    hFacC      (tile, k, j, i) float32 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0\n",
       "  * tile       (tile) int32 7 8 9\n",
       "    time_bnds  (time, nv) datetime64[ns] 2010-01-01 2010-02-01 ... 2011-01-01\n",
       "    iter       (time) int32 158532 159204 159948 160668 ... 165084 165804 166548\n",
       "  * time       (time) datetime64[ns] 2010-01-16T12:00:00 ... 2010-12-16T12:00:00\n",
       "Dimensions without coordinates: nv\n",
       "Data variables:\n",
       "    THETA      (time, tile, k, j, i) float32 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "theta_dir= ECCO_dir + '/nctiles_monthly/THETA/'\n",
    "theta_subset = ecco.load_ecco_var_from_years_nc(theta_dir, \\\n",
    "                                                'THETA', years_to_load = [2010], \\\n",
    "                                                tiles_to_load = [ 7,8,9], \\\n",
    "                                                k_subset = [0,1,2,3,4]).load()\n",
    "theta_subset.attrs = []\n",
    "theta_subset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As expected, ``theta_subset`` has 3 tiles and 5 vertical levels."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading multiple years of single ECCOv4 variable using ``load_ecco_var_from_years_nc``\n",
    "\n",
    "Another benefit of ``load_ecco_var_from_years`` is that you can load more than one year of output.  First we'll show loading *two* years then *all* of the years of output available in the file directory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:    (i: 90, j: 90, nv: 2, tile: 13, time: 24)\n",
       "Coordinates:\n",
       "    rA         (tile, j, i) float32 362256450.0 363300960.0 ... 361119100.0\n",
       "    XC         (tile, j, i) float32 -111.60647 -111.303 ... -111.86579\n",
       "    YC         (tile, j, i) float32 -88.24259 -88.382515 ... -88.07871 -88.10267\n",
       "  * i          (i) int32 0 1 2 3 4 5 6 7 8 9 ... 80 81 82 83 84 85 86 87 88 89\n",
       "  * j          (j) int32 0 1 2 3 4 5 6 7 8 9 ... 80 81 82 83 84 85 86 87 88 89\n",
       "  * tile       (tile) int32 0 1 2 3 4 5 6 7 8 9 10 11 12\n",
       "    time_bnds  (time, nv) datetime64[ns] 2010-01-01 2010-02-01 ... 2012-01-01\n",
       "    iter       (time) int32 158532 159204 159948 160668 ... 173844 174564 175308\n",
       "  * time       (time) datetime64[ns] 2010-01-16T12:00:00 ... 2011-12-16T12:00:00\n",
       "Dimensions without coordinates: nv\n",
       "Data variables:\n",
       "    SSH        (time, tile, j, i) float32 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# two years:  2010 and 2011\n",
    "SSH_2010_2011 = ecco.load_ecco_var_from_years_nc(SSH_dir, \\\n",
    "                                                'SSH', \n",
    "                                                 years_to_load = [2010, 2011]).load()\n",
    "SSH_2010_2011.attrs = []\n",
    "SSH_2010_2011"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that SSH_2010_2011 has 24 time records"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:    (i: 90, j: 90, nv: 2, tile: 13, time: 288)\n",
       "Coordinates:\n",
       "    rA         (tile, j, i) float32 362256450.0 363300960.0 ... 361119100.0\n",
       "    XC         (tile, j, i) float32 -111.60647 -111.303 ... -111.86579\n",
       "    YC         (tile, j, i) float32 -88.24259 -88.382515 ... -88.07871 -88.10267\n",
       "  * i          (i) int32 0 1 2 3 4 5 6 7 8 9 ... 80 81 82 83 84 85 86 87 88 89\n",
       "  * j          (j) int32 0 1 2 3 4 5 6 7 8 9 ... 80 81 82 83 84 85 86 87 88 89\n",
       "  * tile       (tile) int32 0 1 2 3 4 5 6 7 8 9 10 11 12\n",
       "    time_bnds  (time, nv) datetime64[ns] 1992-01-01 1992-02-01 ... 2015-12-31\n",
       "    iter       (time) int32 732 1428 2172 2892 ... 208164 208908 209628 210360\n",
       "  * time       (time) datetime64[ns] 1992-01-16T12:00:00 ... 2015-12-16T12:00:00\n",
       "Dimensions without coordinates: nv\n",
       "Data variables:\n",
       "    SSH        (time, tile, j, i) float32 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# all years\n",
    "SSH_all = ecco.load_ecco_var_from_years_nc(SSH_dir, \\\n",
    "                                           'SSH', \n",
    "                                           years_to_load = 'all').load()\n",
    "SSH_all.attrs = []\n",
    "SSH_all"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have all 288 month records of SSH."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading one or more years of more than variable using ``recursive_load_ecco_var_from_years_nc``\n",
    "\n",
    "With ``recursive_load_ecco_var_from_years_nc`` one can specify one or more variables to load, one or more years to load, while also requesting only a subset of tiles and vertical levels.  \n",
    "\n",
    "Let's demonstrate by first loading all of the information for monthly-averaged ``SSH`` and ``THETA`` fields for the years 2010, 2011, and 2012.  We'll then load SSH and OBP for *all* of the years and tile 6.  Finally, we'll load all of the monthly-mean variables for the year 2010.\n",
    "\n",
    "### Loading SSH and THETA for 2010-2012"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def is_year_tryexcept(s):\n",
    "    try:\n",
    "        return int(s) >= 1900\n",
    "    except ValueError:\n",
    "        return False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n",
      "False\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "x='ADVr_SLT_1992'\n",
    "y=x.split('_')\n",
    "for yy in y:\n",
    "    print(is_year_tryexcept(yy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading files of  THETA\n",
      "loading files of  SSH\n"
     ]
    }
   ],
   "source": [
    "nctiles_monthly_dir = ECCO_dir + 'nctiles_monthly/'\n",
    "SSH_THETA_2010_2012 = \\\n",
    "        ecco.recursive_load_ecco_var_from_years_nc(nctiles_monthly_dir, \\\n",
    "                                                   vars_to_load=['SSH','THETA'], \\\n",
    "                                                   years_to_load = [2010, 2011, 2012], less_output=True).load()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:    (i: 90, j: 90, k: 50, nv: 2, tile: 13, time: 36)\n",
       "Coordinates:\n",
       "    rA         (tile, j, i) float32 362256450.0 363300960.0 ... 361119100.0\n",
       "    drF        (k) float32 10.0 10.0 10.0 10.0 10.0 ... 387.5 410.5 433.5 456.5\n",
       "    YC         (tile, j, i) float32 -88.24259 -88.382515 ... -88.07871 -88.10267\n",
       "    PHrefC     (k) float32 49.05 147.15 245.25 ... 49435.043 53574.863 57940.312\n",
       "    Z          (k) float32 -5.0 -15.0 -25.0 -35.0 ... -5039.25 -5461.25 -5906.25\n",
       "    hFacC      (tile, k, j, i) float32 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0\n",
       "    XC         (tile, j, i) float32 -111.60647 -111.303 ... -111.86579\n",
       "  * i          (i) int32 0 1 2 3 4 5 6 7 8 9 ... 80 81 82 83 84 85 86 87 88 89\n",
       "  * j          (j) int32 0 1 2 3 4 5 6 7 8 9 ... 80 81 82 83 84 85 86 87 88 89\n",
       "  * k          (k) int32 0 1 2 3 4 5 6 7 8 9 ... 40 41 42 43 44 45 46 47 48 49\n",
       "  * tile       (tile) int32 0 1 2 3 4 5 6 7 8 9 10 11 12\n",
       "    time_bnds  (time, nv) datetime64[ns] 2010-01-01 2010-02-01 ... 2013-01-01\n",
       "    iter       (time) int32 158532 159204 159948 160668 ... 182628 183348 184092\n",
       "  * time       (time) datetime64[ns] 2010-01-16T12:00:00 ... 2012-12-16T12:00:00\n",
       "Dimensions without coordinates: nv\n",
       "Data variables:\n",
       "    THETA      (time, tile, k, j, i) float32 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0\n",
       "    SSH        (time, tile, j, i) float32 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0\n",
       "Attributes:\n",
       "    Conventions:                  CF-1.6\n",
       "    Insitution:                   JPL\n",
       "    Metadata_Conventions:         CF-1.6, Unidata Dataset Discovery v1.0, GDS...\n",
       "    Project:                      Estimating the Circulation and Climate of t...\n",
       "    author:                       Ian Fenty and Ou Wang\n",
       "    cdm_data_type:                Grid\n",
       "    date_created:                 Mon May 13 18:32:24 2019\n",
       "    geospatial_lat_max:           89.739395\n",
       "    geospatial_lat_min:           -89.873055\n",
       "    geospatial_lat_units:         degrees_north\n",
       "    geospatial_lon_max:           179.98691\n",
       "    geospatial_lon_min:           -179.98895\n",
       "    geospatial_lon_units:         degrees_east\n",
       "    geospatial_vertical_max:      -5.0\n",
       "    geospatial_vertical_min:      -5906.25\n",
       "    geospatial_vertical_units:    meter\n",
       "    no_data:                      NaNf\n",
       "    nx:                           90\n",
       "    ny:                           90\n",
       "    nz:                           50\n",
       "    product_time_coverage_end:    2015-12-31T12:00:00\n",
       "    product_time_coverage_start:  1992-01-01T12:00:00\n",
       "    product_version:              ECCO Version 4 Release 3 (ECCOv4r3) 1992-2015\n",
       "    time_coverage_end:            2013-01-01T00:00:00\n",
       "    time_coverage_start:          2010-01-01T00:00:00\n",
       "    time_units:                   days since 1992-01-01 00:00:00"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SSH_THETA_2010_2012"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see three years (36 months) of data and 13 tiles and 50 vertical levels."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading SSH and OBP for all years and tile 6\n",
    "Now let's demonstrate how the ``recursive_load_ecco_var_from_years_nc`` routine enable us to load all of the years of output for multiple variables.  The trick is to specify the directory that contains all of the variables.  The routine will recursively search all subdirectories for these fields.  Note, this only works if the subdirectories are of the same temporal period (monthly mean, daily mean, or snapshots).  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading files of  SSH\n",
      "loading files of  OBP\n"
     ]
    }
   ],
   "source": [
    "SSH_OBP_2010_tile_6 = \\\n",
    "        ecco.recursive_load_ecco_var_from_years_nc(nctiles_monthly_dir, \\\n",
    "                                                   vars_to_load=['SSH', 'OBP'], \\\n",
    "                                                   years_to_load = 'all', \\\n",
    "                                                   tiles_to_load = 6).load()                                           "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:    (i: 90, j: 90, nv: 2, tile: 1, time: 288)\n",
       "Coordinates:\n",
       "    rA         (tile, j, i) float32 246414940.0 412417600.0 ... 246414940.0\n",
       "    XC         (tile, j, i) float32 52.0 52.331654 ... -127.66834 -128.0\n",
       "    YC         (tile, j, i) float32 67.57341 67.67698 ... 67.67698 67.57341\n",
       "  * i          (i) int32 0 1 2 3 4 5 6 7 8 9 ... 80 81 82 83 84 85 86 87 88 89\n",
       "  * j          (j) int32 0 1 2 3 4 5 6 7 8 9 ... 80 81 82 83 84 85 86 87 88 89\n",
       "  * tile       (tile) int32 6\n",
       "    time_bnds  (time, nv) datetime64[ns] 1992-01-01 1992-02-01 ... 2015-12-31\n",
       "    iter       (time) int32 732 1428 2172 2892 ... 208164 208908 209628 210360\n",
       "  * time       (time) datetime64[ns] 1992-01-16T12:00:00 ... 2015-12-16T12:00:00\n",
       "Dimensions without coordinates: nv\n",
       "Data variables:\n",
       "    SSH        (time, tile, j, i) float32 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0\n",
       "    OBP        (time, tile, j, i) float32 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0\n",
       "Attributes:\n",
       "    Conventions:                  CF-1.6\n",
       "    Insitution:                   JPL\n",
       "    Metadata_Conventions:         CF-1.6, Unidata Dataset Discovery v1.0, GDS...\n",
       "    Project:                      Estimating the Circulation and Climate of t...\n",
       "    author:                       Ian Fenty and Ou Wang\n",
       "    cdm_data_type:                Grid\n",
       "    date_created:                 Tue May 14 10:19:12 2019\n",
       "    geospatial_lat_max:           89.739395\n",
       "    geospatial_lat_min:           67.57341\n",
       "    geospatial_lat_units:         degrees_north\n",
       "    geospatial_lon_max:           179.9739\n",
       "    geospatial_lon_min:           -179.98895\n",
       "    geospatial_lon_units:         degrees_east\n",
       "    geospatial_vertical_max:      0\n",
       "    geospatial_vertical_min:      0\n",
       "    geospatial_vertical_units:    meter\n",
       "    no_data:                      NaNf\n",
       "    nx:                           90\n",
       "    ny:                           90\n",
       "    nz:                           1\n",
       "    product_time_coverage_end:    2015-12-31T12:00:00\n",
       "    product_time_coverage_start:  1992-01-01T12:00:00\n",
       "    product_version:              ECCO Version 4 Release 3 (ECCOv4r3) 1992-2015\n",
       "    time_coverage_end:            2015-12-31T00:00:00\n",
       "    time_coverage_start:          1992-01-01T00:00:00\n",
       "    time_units:                   days since 1992-01-01 00:00:00"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SSH_OBP_2010_tile_6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading the entire (or large fractions) of the entire ECCOv4 solution using Dask\n",
    "\n",
    "We can load the entire ECCOv4 solution into our workspace and perform calcuations with the entire solution thanks to the amazing ``Dask`` library implemented in the ``xarray`` package.  Why is this useful?  Because it is unlikely that the machine you are working on has enough RAM to load the entire ECCOv4 solution at one time.  By using ``Dask`` we can *virtually* load all of the ECCO fields into memory.  ``Dask`` even enables us to do calculations with these fields even though the entire data is never stored in memory.  \n",
    "\n",
    "Here is some more information about these features from the ``Dask`` website, \n",
    "https://docs.dask.org/\n",
    "\n",
    "1. **Larger-than-memory:** Lets you work on datasets that are larger than your available memory by breaking up your array into many small pieces, operating on those pieces in an order that minimizes the memory footprint of your computation, and effectively streaming data from disk.*\n",
    "\n",
    "2. **Blocked Algorithms:** Perform large computations by performing many smaller computations\n",
    "\n",
    "Finally, in cluster environments ``Dask`` can distribute computations across many cores to speed up large redundant calculation.  \n",
    "\n",
    "An in-depth description of ``Dask`` is outside the scope of this tutorial.  For the moment, let us compare the operation of loading **all** the 2D daily-mean fields for 2010 both using ``Dask`` and without using ``Dask``.  Without ``Dask`` these fields will be loaded into memory.  With ``Dask`` we will only load a minimum of the ``Datasets``, the Dimensions and Coordinates.\n",
    "\n",
    "To demonstate some of the advantages of using ``DASK`` to load and analyze ECCO fields, let's load several monthly-mean variable for two years with and without ``Dask``.  Then we'll load the monthly-mean fields for four years using Dask.  At the end we'll compare their *times to load* and *memory footprints*.\n",
    "\n",
    "### Example 1a: Load two years monthly-mean ECCO fields into memory without ``Dask``\n",
    "\n",
    "The ``.load()`` suffix appended to the end of these load commands is a command to fully load fields into memory.  Without it, ``Dask`` only *virtually* loads the fields."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading files of  THETA\n",
      "loading files of  SSH\n",
      "loading files of  UVEL\n",
      "loading files of  VVEL\n",
      "loading files of  ADVr_SLT\n",
      "loading files of  ADVr_TH\n",
      "loading files of  ADVxHEFF\n",
      "loading files of  ADVxSNOW\n",
      "loading files of  ADVx_SLT\n",
      "loading files of  SIarea\n",
      "loading files of  OBP\n",
      "loading files of  SIheff\n",
      "11.277666807174683\n"
     ]
    }
   ],
   "source": [
    "mon_mean_dir = ECCO_dir + 'nctiles_monthly/'\n",
    "import time\n",
    "t_0 = time.time()\n",
    "\n",
    "large_subset_no_dask = \\\n",
    "    ecco.recursive_load_ecco_var_from_years_nc(mon_mean_dir, \\\n",
    "                                               vars_to_load=['THETA','SSH','UVEL','VVEL',\\\n",
    "                                                             'ADVr_SLT','ADVr_TH',\\\n",
    "                                                             'ADVx_SLT','ADVy_SLT',\\\n",
    "                                                             'ADVxHEFF','ADVxSNOW', \\\n",
    "                                                             'OBP','SIarea','SIheff'],\n",
    "                                               years_to_load=[2010, 2011],\\\n",
    "                                               less_output=True).load()\n",
    "\n",
    "delta_t_2_yrs_no_dask =  time.time() - t_0\n",
    "print(delta_t_2_yrs_no_dask)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### Example 1b: Load two years of monthly-mean ECCO fields into memory using ``Dask``\n",
    "\n",
    "This time we will omit the ``.load()`` suffix and use ``Dask``."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading files of  THETA\n",
      "loading files of  SSH\n",
      "loading files of  UVEL\n",
      "loading files of  VVEL\n",
      "loading files of  ADVr_SLT\n",
      "loading files of  ADVr_TH\n",
      "loading files of  ADVxHEFF\n",
      "loading files of  ADVxSNOW\n",
      "loading files of  ADVx_SLT\n",
      "loading files of  SIarea\n",
      "loading files of  OBP\n",
      "loading files of  SIheff\n",
      "3.1377756595611572\n"
     ]
    }
   ],
   "source": [
    "t_0 = time.time()\n",
    "large_subset_with_dask = ecco.recursive_load_ecco_var_from_years_nc(mon_mean_dir, \\\n",
    "                                           vars_to_load=['THETA','SSH','UVEL','VVEL',\\\n",
    "                                                         'ADVr_SLT','ADVr_TH',\\\n",
    "                                                         'ADVx_SLT','ADVy_SLT',\\\n",
    "                                                         'ADVxHEFF','ADVxSNOW', \\\n",
    "                                                         'OBP','SIarea','SIheff'],\n",
    "                                           years_to_load=[2010, 2011])\n",
    "delta_t_2_yrs_with_dask =  time.time() - t_0\n",
    "print(delta_t_2_yrs_with_dask)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example 2: Load 5 years of monthly-mean field with ``DASK``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading files of  THETA\n",
      "loading files of  SSH\n",
      "loading files of  UVEL\n",
      "loading files of  VVEL\n",
      "loading files of  ADVr_SLT\n",
      "loading files of  ADVr_TH\n",
      "loading files of  ADVxHEFF\n",
      "loading files of  ADVxSNOW\n",
      "loading files of  ADVx_SLT\n",
      "loading files of  SIarea\n",
      "loading files of  OBP\n",
      "loading files of  SIheff\n",
      "6.462316274642944\n"
     ]
    }
   ],
   "source": [
    "t_0 = time.time()\n",
    "all_fields_with_dask = ecco.recursive_load_ecco_var_from_years_nc(mon_mean_dir, \\\n",
    "                                           vars_to_load=['THETA','SSH','UVEL','VVEL',\\\n",
    "                                                         'ADVr_SLT','ADVr_TH',\\\n",
    "                                                         'ADVx_SLT','ADVy_SLT',\\\n",
    "                                                         'ADVxHEFF','ADVxSNOW', \\\n",
    "                                                         'OBP','SIarea','SIheff'], \\\n",
    "                                           years_to_load=[2008, 2009, 2010, 2011, 2012], \\\n",
    "                                           dask_chunk=True)\n",
    "delta_t_5_yrs_with_dask =  time.time() - t_0\n",
    "print (delta_t_5_yrs_with_dask)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Results\n",
    "\n",
    "Now we examine the time it took to load these fields and the comparative memory footprints"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loaded 2 years without dask in  11.0 sec\n",
      "loaded 2 years with_dask in     3.0 sec\n",
      "loaded 5 years with_dask in     6.0 sec\n"
     ]
    }
   ],
   "source": [
    "print ('loaded 2 years without dask in ', np.round(delta_t_2_yrs_no_dask), 'sec')\n",
    "print ('loaded 2 years with_dask in    ', np.round(delta_t_2_yrs_with_dask), 'sec')\n",
    "print ('loaded 5 years with_dask in    ', np.round(delta_t_5_yrs_with_dask), 'sec')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The real advantage of using Dask comes when examining the size of these objects in memory.  The 'pympler' package allows you to see the memory footprint of these objects.\n",
    "\n",
    "https://pythonhosted.org/Pympler/index.html\n",
    "\n",
    "To proceed, install the pympler library into your Python environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "large_subset_no_dask   :  3120.0 mb\n",
      "large_subset_with_dask :  171.0 mb\n",
      "all_fields_with_dask   :  172.0 mb\n"
     ]
    }
   ],
   "source": [
    "# Estimate the memory footprint in MB\n",
    "from pympler import asizeof\n",
    "\n",
    "s = 0\n",
    "F = large_subset_no_dask\n",
    "for i in F.variables.keys():\n",
    "    s += asizeof.asizeof(large_subset_no_dask[i])\n",
    "print('large_subset_no_dask   : ', np.round(s/2**20), 'mb')\n",
    "\n",
    "F = large_subset_with_dask\n",
    "s = 0\n",
    "for i in F.variables.keys():\n",
    "    s += asizeof.asizeof(F[i])\n",
    "print('large_subset_with_dask : ', np.round(s/2**20), 'mb')\n",
    "\n",
    "F = all_fields_with_dask\n",
    "s = 0\n",
    "for i in F.variables.keys():\n",
    "    s += asizeof.asizeof(F[i])\n",
    "print('all_fields_with_dask   : ', np.round(s/2**20), 'mb')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using ``Dask``, we were able to load 5 years of data faster than 2 years without Dask.\n",
    "\n",
    "In terms of memory, the 5 years of data in *all_fields_with_dask* object takes a fraction of the memory of the 2 years of data in *large_subset_no_dask*.  With much less memory reserved to hold all of the fields, we have more memory avaiable for calculations on the parts of the fields that we care about.\n",
    "\n",
    "Go ahead and experiment with using ``Dask`` to load the daily-averaged fields.  Because all of the daily-averaged fields in the standard ECCO product are 2D, loading them with ``Dask`` takes very little time!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary\n",
    "\n",
    "Now you know how effecient ways to load ECCOv4 NetCDF files, both when you are reading variables split into *tiles*  and when you are reading variables aggregated by year.\n",
    "\n",
    "Using ``Dask`` we showed that one can prepare a work environment where ECCO model variables are accessible for calculations even without fully loading the fields into memory."
   ]
  }
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